• KIPS Transactions on Software and Data Engineering
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• v.3 no.8
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• pp.299-308
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• 2014

Various literatures related computing of information processing have been recently shown the researches inspired from the remarkably excellent human capabilities which recognize and categorize very complex visual patterns such as body motions and facial expressions. Applied from human's outstanding ability of perception, the classification function of visual sequences without context information is specially crucial task for computer vision to understand both the coding and the retrieval of spatio-temporal patterns. This paper presents a biological process based action recognition model of computer vision, which is inspired from visual information processing of human brain for action recognition of visual sequences. Proposed model employs the structure of neural fields of bio-inspired visual perception on detecting motion sequences and discriminating visual patterns in human brain. Experimental results show that proposed recognition model takes not only into account several biological properties of visual information processing, but also is tolerant of time-warping. Furthermore, the model allows robust temporal evolution of classification compared to researches of action recognition. Presented model contributes to implement bio-inspired visual processing system such as intelligent robot agent, etc.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.423-426
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• 2003

In this paper, we use the modular neural network and recurrent neural network structure to implement the artificial brain information processing. We also select related adaptive learning methods to learn the entirely new input in the existed neural network. With this, a part of information process in brain is implemented as and autonomous and adaptive model by neural network and further more, the entire model for information process in brain can be introduced.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.12
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• pp.7187-7191
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• 2013

Most of the exogenous and endogenous chemical compounds are metabolized by enzymes of xenobiotic processing pathways, including the phase I cytochrome p450 species. Carcinogens and their metabolites are generally detoxified by phase II enzymes like glutathione-S-transferases (GST). The balance of enzymes determines whether metabolic activation of pro-carcinogens or inactivation of carcinogens occurs. Under certain conditions, deregulated expression of xenobiotic enzymes may also convert endogenous substrates to metabolites that can facilitate DNA adduct formation and ultimately lead to cancer development. In this study, we aimed to test the association between deregulation of metabolizing genes and brain tumorigenesis. The expression profile of metabolizing genes CYP1A1 and GSTP1 was therefore studied in a cohort of 36 brain tumor patients and controls using Western blotting. In a second part of the study we analyzed protein expression of GSTs in the same study cohort by ELISA. CYP1A1 expression was found to be significantly high (p<0.001) in brain tumor as compared to the normal tissues, with ~4 fold (OR=4, 95%CI=0.43-37) increase in some cases. In contrast, the expression of GSTP1 was found to be significantly low in brain tumor tissues as compared to the controls (p<0.02). This down regulation was significantly higher (OR=0.05, 95%CI=0.006-0.51; p<0.007) in certain grades of lesions. Furthermore, GSTs levels were significantly down-regulated (p<0.014) in brain tumor patients compared to controls. Statistically significant decrease in GST levels was observed in the more advanced lesions (III-IV, p<0.005) as compared to the early tissue grades (I-II). Thus, altered expression of these xenobiotic metabolizing genes may be involved in brain tumor development in Pakistani population. Investigation of expression of these genes may provide information not only for the prediction of individual cancer risk but also for the prevention of cancer.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.11a
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• pp.1064-1065
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• 2020

본 연구는 한국인 뇌MRI영상을 이용하여 대뇌 영역별 분석 프로토콜과 정량 평가방법을 개발하여 정상인을 대상으로 뇌용적량을 정량 분석하고자 한다. 뇌MRI영상 분석 프로토콜을 최적화하기 위해 먼저 뇌용적 변화에 있어 평가방법을 선정하고, VBM 후처리과정은 MRI영상 신호불균질성 교정, 조직세분화 방법, 대뇌 표준영상 제작, 신호 편평화(smoothing) 과정을 단계별로 최적화하였다. 이 정량분석 프로토콜은 정상인과 뇌질환 환자의 뇌용적 비교뿐만 아니라 환자 약물 치료 전·후에 나타나는 용적 변화를 정량적으로 평가하는 연구에 활용할 수 있을 것으로 기대한다.

• Journal of the Korea Society of Computer and Information
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• v.21 no.12
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• pp.11-18
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• 2016

Magnetic resonance image (MRI) is widely used in brain research field and medical image. Especially, non-invasive brain activation acquired image technique, which is functional magnetic resonance image (fMRI) is used in brain study. In this study, we investigate brain activation occurred by LED light stimulation. For investigate of brain activation in experimental small animal, we used high magnetic field 9.4T MRI. Experimental small animal is Balb/c mouse, method of fMRI is using echo planar image (EPI). EPI method spend more less time than any other MRI method. For this reason, however, EPI data has low contrast. Due to the low contrast, image pre-processing is very hard and inaccuracy. In this study, we planned the study protocol, which is called block design in fMRI research field. The block designed has 8 LED light stimulation session and 8 rest session. All block is consist of 6 EPI images and acquired 1 slice of EPI image is 16 second. During the light session, we occurred LED light stimulation for 1 minutes 36 seconds. During the rest session, we do not occurred light stimulation and remain the light off state for 1 minutes 36 seconds. This session repeat the all over the EPI scan time, so the total spend time of EPI scan has almost 26 minutes. After acquired EPI data, we performed the analysis of this image data. In this study, we analysis of EPI data using statistical parametric map (SPM) software and performed image pre-processing such as realignment, co-registration, normalization, smoothing of EPI data. The pre-processing of fMRI data have to segmented using this software. However this method has 3 different method which is Gaussian nonparametric, warped modulate, and tissue probability map. In this study we performed the this 3 different method and compared how they can change the result of fMRI analysis results. The result of this study show that LED light stimulation was activate superior colliculus region in mouse brain. And the most higher activated value of segmentation method was using tissue probability map. this study may help to improve brain activation study using EPI and SPM analysis.

• Proceedings of the IEEK Conference
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• 2001.06e
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• pp.203-206
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• 2001

The fMRI which can express the function of brain as MR image is now being studied. The study on the functional image has usually been performed with the MRI in 4 tesla class in goneral, but if gradient echo imaging method could be used, it might make the most of what it has with the MRI in 1.5 tesla class. However, the lack of adequate image post-processing software prevents it from being used as widely as it could be. For the image post-processing algorithm of the functional image, subtraction method and several statistical methods are used with continuous introduction of new method recently. In this paper, we suggest adaptation of wavelet algorithm for obtaining a more reliable brain function map.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.17 no.1
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• pp.38-46
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• 2024

In general, EEG signal analysis has been the subject of several studies due to its ability to provide an objective mode of recording brain stimulation, which is widely used in brain-computer interface research with applications in medical diagnosis and rehabilitation engineering. In this study, we developed EEG reception hardware to measure electroencephalograms and implemented a processing system, classifying it into server and data processing. It was conducted as an intermediate-stage research on the implementation of a brain-computer interface using electroencephalograms, and was implemented in the form of predicting the user's arm movements according to measured electroencephalogram data. Electroencephalogram measurements were performed using input from four electrodes through an analog-to-digital converter. After sending this to the server through a communication process, we designed and implemented a system flow in which the server classifies the electroencephalogram input using a convolutional neural network model and displays the results on the user terminal.

• Korean Journal of Biological Psychiatry
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• v.3 no.1
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• pp.3-13
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• 1996

Many studies have been conducted to search for the anatomical abnormalities in the brain which ore etiologically related with schizophrenia. Generally schizophrenia in known to be related with decreased brain tissue, hypofrontality and abnormalities in the temporal lobe including the hippocamypus, the agmygdala and the entorhinal cortex. Other areas related with the disorder ore basal ganglia, thalamus, brain stem, pons and nucleus accumbens. Abnormality in brain asymmetry is one of the new areas of interest which needs further study. The results so for ore inconsistent and it is unlikely that the abnormality in one structure is the only cause of the disorder. Rather, schizophrenia develops from the impairment of the parallel processing of integrated and reciprocal information which is distributed to the multiple structures. Histopathologic studies in the postmortem brain suggest that schizophrenia is related with neurodevelopmental abnormality rather than neurodegenerative abnormality.

• The KIPS Transactions:PartA
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• v.8A no.4
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• pp.359-366
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• 2001

This paper overviews the design of SANique$^{TM}$ -a shred file system for Linux cluster based on SAN environment. SANique$^{TM}$ has the capability of transferring user data from network-attached SAN disks to client applcations directly without the control of centralized file server system. The paper also presents the characteristics of each SANique$^{TM}$ subsystem: CFM(Cluster File Manager), CVM(Cluster Volume Manager), CLM(Cluster Lock Manager), CBM(Cluster Buffer Manager) and CRM(Cluster Recovery Manager). Under the SANique$^{TM}$ design layout, then, the syndrome of '||'&'||'quot;split-brain'||'&'||'quot; in shared file system environments is described and defined. The work first generalizes and illustrates possible situations in each of which a shared file system environment may split into two or more pieces of separate brain. Finally, the work describes the SANique$^{TM}$ approach to the given "split-brain"problem using SAN disk named "split-brain" and develops the overall recovery procedure of shared file systems.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.11a
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• pp.1673-1675
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• 2013

인간 활동의 전 영역을 총괄하는 대뇌정보기능을 대표하는 뇌파는 대뇌피질에서 발현된다고 알려져 있다. 의학적인 연구 결과에 의하면 인지 사고 등의 역동적인 지식 활동, 다양한 감성 행동, 및 고차원적인 정신활동까지도 뇌파 분석을 통해서 어느 정도는 기계적인 인식이 가능한 것으로 알려져 있다. 뇌-컴퓨터 인터페이스는 인간 중심의 시스템을 위한 핵심 연구로서 뇌파 신호 분석에 의한 사용자 의도 인식 시스의 개발을 목표로 한다. 이에 따라서, 범용적으로 적용 가능한 뇌파신호 분석 기법 및 자동 처리 시스템에 관한 연구가 활발히 진행 중이다. 특히, 뇌는 부위별로 그 기능이 세분화 되어 있으며 의식 상태와 정신활동에 따라 뇌파가 수시로 변하면서 특정한 패턴을 갖는다. 이러한 뇌의 정보처리 메커니즘을 밝혀내면 전자장치와의 통신 인터페이스를 통해 기기를 제어할 수 있다. 본 논문은 사용자의 의도를 분석하는 방법과 이를 통해 다른 장치의 인터페이스를 제어할 수 있는 시스템을 설계했다.